To train and validate the neural network, \(32 \times 32\)  patches centered on the contour of handwritten images in the ICDAR13 train set are extracted. We sample around \(5000000/25000\)  image patches for train/validation which are subsequently passed forward to the network. ResNet weights are initialized by He-initialization [1]} and Xavier-initilization [2]} used to initialize Conv layer of the NetVLAD. As the ICDAR13 test set consists of 100 writers, the number of cluster centers has been set to 100 in the NetVLAD layer. The proposed neural network is optimized using Adamax with respect to triplet semi-hard loss with margin \(m =0.1\) , decay rates \(\beta _{1} = 0.9\)  and \(\beta _{2} = 0.99\)  for 1st moment estimate and exponentially weighted infinity norm, respectively. Training is stopped after 5 epochs since the loss value stagnated at this point. The Visualization of learning curves can be seen in Figure REF .
